• español
  • English
  • français
  • Deutsch
  • português (Brasil)
  • italiano
    • español
    • English
    • français
    • Deutsch
    • português (Brasil)
    • italiano
    • español
    • English
    • français
    • Deutsch
    • português (Brasil)
    • italiano
    JavaScript is disabled for your browser. Some features of this site may not work without it.

    Listar

    Todo UVaDOCComunidadesPor fecha de publicaciónAutoresMateriasTítulos

    Mi cuenta

    Acceder

    Estadísticas

    Ver Estadísticas de uso

    Compartir

    Ver ítem 
    •   UVaDOC Principal
    • PRODUCCIÓN CIENTÍFICA
    • Departamentos
    • Dpto. Estadística e Investigación Operativa
    • DEP24 - Artículos de revista
    • Ver ítem
    •   UVaDOC Principal
    • PRODUCCIÓN CIENTÍFICA
    • Departamentos
    • Dpto. Estadística e Investigación Operativa
    • DEP24 - Artículos de revista
    • Ver ítem
    • español
    • English
    • français
    • Deutsch
    • português (Brasil)
    • italiano

    Exportar

    RISMendeleyRefworksZotero
    • edm
    • marc
    • xoai
    • qdc
    • ore
    • ese
    • dim
    • uketd_dc
    • oai_dc
    • etdms
    • rdf
    • mods
    • mets
    • didl
    • premis

    Citas

    Por favor, use este identificador para citar o enlazar este ítem:http://uvadoc.uva.es/handle/10324/11617

    Título
    Robust estimation of mixtures of regressions with random covariates, via trimming and constraints
    Autor
    García Escudero, Luis ÁngelAutoridad UVA Orcid
    Gordaliza Ramos, AlfonsoAutoridad UVA Orcid
    Greselin, Francesca
    Ingrassia, Salvatore
    Mayo Iscar, AgustínAutoridad UVA Orcid
    Año del Documento
    2015
    Editorial
    Universidad de Valladolid. Facultad de Medicina
    Descripción
    Producción Científica
    Documento Fuente
    Arxiv, Febrero 2015, vol.1, p.1-30
    Resumen
    A robust estimator for a wide family of mixtures of linear regression is presented. Robustness is based on the joint adoption of the Cluster Weighted Model and of an estimator based on trimming and restrictions. The selected model provides the conditional distribution of the response for each group, as in mixtures of regression, and further supplies local distributions for the explanatory variables. A novel version of the restrictions has been devised, under this model, for separately controlling the two sources of variability identified in it. This proposal avoids singularities in the log-likelihood, caused by approximate local collinearity in the explanatory variables or local exact fits in regressions, and reduces the occurrence of spurious local maximizers. In a natural way, due to the interaction between the model and the estimator, the procedure is able to resist the harmful influence of bad leverage points along the estimation of the mixture of regressions, which is still an open issue in the literature. The given methodology defines a well-posed statistical problem, whose estimator exists and is consistent to the corresponding solution of the population optimum, under widely general conditions. A feasible EM algorithm has also been provided to obtain the corresponding estimation. Many simulated examples and two real datasets have been chosen to show the ability of the procedure, on the one hand, to detect anomalous data, and, on the other hand, to identify the real cluster regressions without the influence of contamination. Keywords Cluster Weighted Modeling · Mixture of Regressions · Robustness
    Materias (normalizadas)
    Análisis multivariante
    Revisión por pares
    SI
    Idioma
    eng
    URI
    http://uvadoc.uva.es/handle/10324/11617
    Derechos
    openAccess
    Aparece en las colecciones
    • DEP24 - Artículos de revista [78]
    Mostrar el registro completo del ítem
    Ficheros en el ítem
    Nombre:
    AgustinMayo1.pdf
    Tamaño:
    508.0Kb
    Formato:
    Adobe PDF
    Descripción:
    PD-250
    Thumbnail
    Visualizar/Abrir
    Attribution-NonCommercial-NoDerivatives 4.0 InternationalLa licencia del ítem se describe como Attribution-NonCommercial-NoDerivatives 4.0 International

    Universidad de Valladolid

    Powered by MIT's. DSpace software, Version 5.10